Algorithmic Game Theory
- Department of Computer Science
This course extends the knowledge in multiagent systems and game theory by focusing on the algorithmic and computational problems - the computational complexity and current algorithms for finding and approximating different solution concepts, the impact of different representations of games, and the applications of learning techniques in game theory.
The course is suitable for students that have already completed the course on Multiagent Systems (A4M36MAS) and either wish to strengthen their knowledge in game theory, or they are working on related problems from artificial intelligence such as machine learning, decision theory, planning.
It is recommended that students have completed the course on multiagent systems (A4M36MAS) or have knowledge about the game theory corresponding to that course.
- Syllabus of lectures:
1. Introduction to Game Theory
2. Fundamental Theorems (von Neumann, Nash, Kuhn)
3. Succinct Representations of Games
4. Finding Nash Equilibria
5. Approximating Nash Equilibria
6. Finding Correlated Equilibria
7. Finding Stackelberg Equilibria
8. Repeated Games
9. Learning and Dynamics in Games
10. Learning in Extensive-Form Games
11. Games of Incomplete Information, Auctions
12. Algorithmic Mechanism Design
13. Mechanisms Without Money
14. Stochastic Games
The structure of the lecutres covers the important algorithmic topics in game theory. Besides attending the lectures, the students are assumed to work on their homework assignments that strenghten the understanding of the topic (4h per week).
- Syllabus of tutorials:
- Study Objective:
- Study materials:
Shoham and Leyton-Brown. „Multiagent Systems“, Cambridge University Press, 2009
Nisan, Roughgarden, Tardos and Vazirani. „Algorithmic Game Theory“, Cambridge University Press, 2007
Maschler, Solan and Zamir. „Game Theory“, Cambridge University Press, 2013
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: